Triple
T15244653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurtigruten AS |
E364347
|
entity |
| Predicate | hasShip |
P14595
|
FINISHED |
| Object |
MS Kong Harald
MS Kong Harald is a Norwegian coastal cruise ship operated on the Hurtigruten route, offering passenger and cargo services along Norway’s coastline.
|
E1145547
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: MS Kong Harald | Statement: [Hurtigruten AS, hasShip, MS Kong Harald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MS Kong Harald Context triple: [Hurtigruten AS, hasShip, MS Kong Harald]
-
A.
Kingo
Kingo is a Japanese given name most notably borne by Tatsuno Kingo, a prominent architect of Japan’s Meiji era.
-
B.
Kingo
Kingo is an Eternal who lives on Earth as a charismatic Bollywood movie star while secretly using his cosmic powers to protect humanity.
-
C.
Suliskongen
Suliskongen is a prominent mountain in the Sulitjelma massif of northern Norway, known as one of the major peaks in the Scandinavian border region between Norway and Sweden.
-
D.
König
König is a German-language surname borne by numerous individuals, including notable figures in fields such as religion, science, and the arts.
-
E.
Kœnig
Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: MS Kong Harald Triple: [Hurtigruten AS, hasShip, MS Kong Harald]
Generated description
MS Kong Harald is a Norwegian coastal cruise ship operated on the Hurtigruten route, offering passenger and cargo services along Norway’s coastline.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MS Kong Harald Target entity description: MS Kong Harald is a Norwegian coastal cruise ship operated on the Hurtigruten route, offering passenger and cargo services along Norway’s coastline.
-
A.
Kingo
Kingo is a Japanese given name most notably borne by Tatsuno Kingo, a prominent architect of Japan’s Meiji era.
-
B.
Kingo
Kingo is an Eternal who lives on Earth as a charismatic Bollywood movie star while secretly using his cosmic powers to protect humanity.
-
C.
Suliskongen
Suliskongen is a prominent mountain in the Sulitjelma massif of northern Norway, known as one of the major peaks in the Scandinavian border region between Norway and Sweden.
-
D.
König
König is a German-language surname borne by numerous individuals, including notable figures in fields such as religion, science, and the arts.
-
E.
Kœnig
Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f306f08190be448b215d6c9b6c |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd461cf08190a506aac2f0cec83a |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fedf6ee3f081909553078cd3e9d243 |
completed | May 9, 2026, 7:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fee0016a088190ad87268e035f677e |
completed | May 9, 2026, 7:19 a.m. |
Created at: April 10, 2026, 3:13 a.m.